Task management on fully heterogeneous micro‐server system: Modeling and resolution strategies. (9th September 2018)

• Record Type: Journal Article
• Authors: Zaourar, Lilia; Ait Aba, Massinissa; Briand, David; Philippe, Jean‐Marc
• Other Names: Barbosa Jorge G. guestEditor.; Jeannot Emmanuel guestEditor.; Li Maozhen guestEditor.
• Is Part Of: Concurrency and computation. Volume 30:Number 23(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-09-09
• Subjects: energy consumption -- execution time -- heterogeneous computing -- task mapping and scheduling; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4798
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Many of today's important applications of our everyday lives, eg, weather forecast, design of plane and car shapes, medical analysis, or search engine queries depend on massively parallel computer programs executed in data centers. A large amount of energy is used to power them, and it is of primary importance to compute more efficiently to sustain the increasing demand of computing power while keeping energy consumption reasonable. One promising research path in this domain is heterogeneous systems since specific computing resources (processors, accelerators, etc) are more adapted to efficiently execute parts of applications. Nevertheless, the exploitation of these platforms raises new challenges in terms of application management optimization. The aim of our work is to determine effective algorithms to exploit these heterogeneous platforms by finding appropriate application mapping and scheduling to optimize the execution time and energy consumption with respect to various constraints. To achieve this goal, there is a need of a detailed modeling of the applications and the underlying hardware to be able to find realistic solutions. In this paper, we propose such a model, provide two implementations with state‐of‐the‐art tools, and propose a fast greedy online resolution algorithm and preliminary mapping and scheduling numerical results.